{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年4月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn=db.get_conn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Salary_Mean</th>\n",
       "      <th>Salary_Median</th>\n",
       "      <th>JD_Count</th>\n",
       "      <th>HeadCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201906</td>\n",
       "      <td>13387</td>\n",
       "      <td>12500</td>\n",
       "      <td>95375</td>\n",
       "      <td>306980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201907</td>\n",
       "      <td>13600</td>\n",
       "      <td>12500</td>\n",
       "      <td>91895</td>\n",
       "      <td>293948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201908</td>\n",
       "      <td>13785</td>\n",
       "      <td>12500</td>\n",
       "      <td>91631</td>\n",
       "      <td>289118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201909</td>\n",
       "      <td>13817</td>\n",
       "      <td>12500</td>\n",
       "      <td>87938</td>\n",
       "      <td>277901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201910</td>\n",
       "      <td>13730</td>\n",
       "      <td>12500</td>\n",
       "      <td>84654</td>\n",
       "      <td>269005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201911</td>\n",
       "      <td>13857</td>\n",
       "      <td>12500</td>\n",
       "      <td>81763</td>\n",
       "      <td>262185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201912</td>\n",
       "      <td>13938</td>\n",
       "      <td>12500</td>\n",
       "      <td>81142</td>\n",
       "      <td>260002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202001</td>\n",
       "      <td>14008</td>\n",
       "      <td>12500</td>\n",
       "      <td>75301</td>\n",
       "      <td>242944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>202002</td>\n",
       "      <td>14096</td>\n",
       "      <td>12500</td>\n",
       "      <td>69030</td>\n",
       "      <td>223830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>202003</td>\n",
       "      <td>14220</td>\n",
       "      <td>12500</td>\n",
       "      <td>63574</td>\n",
       "      <td>204853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>202004</td>\n",
       "      <td>14249</td>\n",
       "      <td>12500</td>\n",
       "      <td>112792</td>\n",
       "      <td>367608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Month  Salary_Mean  Salary_Median  JD_Count  HeadCount\n",
       "0   201906        13387          12500     95375     306980\n",
       "1   201907        13600          12500     91895     293948\n",
       "2   201908        13785          12500     91631     289118\n",
       "3   201909        13817          12500     87938     277901\n",
       "4   201910        13730          12500     84654     269005\n",
       "5   201911        13857          12500     81763     262185\n",
       "6   201912        13938          12500     81142     260002\n",
       "7   202001        14008          12500     75301     242944\n",
       "8   202002        14096          12500     69030     223830\n",
       "9   202003        14220          12500     63574     204853\n",
       "10  202004        14249          12500    112792     367608"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat_data = pd.read_sql(sql='select * from MonthlyStats order by Month', con=conn)\n",
    "stat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(\"Month\",\"Salary_Mean\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(\"Month\",\"JD_Count\",data=stat_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts=[]\n",
    "percentages=[]\n",
    "count1=conn.execute(f\"select COUNT(1) from _{year}{month:02}\").fetchall()[0][0]\n",
    "counts.append(count1)\n",
    "\n",
    "for i in range(1,month-6+1):\n",
    "    i_count=conn.execute(f\"select COUNT(1) from _{year}{month:02} a inner join _{year}{month-i:02} b on a.job_id=b.job_id\").fetchall()[0][0]\n",
    "    counts.append(i_count)\n",
    "    percentages.append((counts[i-1]-i_count)/counts[0])\n",
    "    \n",
    "percentages.append(counts[-1]/counts[0])\n",
    "print(percentages)\n",
    "\n",
    "labels=[]\n",
    "for i in range(1,month-6+1):\n",
    "    labels.append(i)\n",
    "labels.append(f\"{str(month-6+1)}+\")\n",
    "\n",
    "plt.pie(percentages, labels=labels)\n",
    "plt.title(\"Age of Jobs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照职能统计平均工资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())\n",
    "\n",
    "data_career=data_original.groupby(by='career').agg(\n",
    "    salary=pd.NamedAgg(column='monthly_salary', aggfunc='mean')\n",
    ")\n",
    "\n",
    "data_career['salary']=data_career['salary'].astype(int)\n",
    "\n",
    "data_career[f'2020年{month}月']=data_career['salary']\n",
    "del data_career['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original2019=pd.read_sql(sql=f\"select * from _{2019}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())\n",
    "\n",
    "data_career2019=data_original2019.groupby(by='career').agg(\n",
    "    salary=pd.NamedAgg(column='monthly_salary', aggfunc='mean')\n",
    ")\n",
    "\n",
    "data_career2019['2019年4月']=data_career2019['salary'].astype(int)\n",
    "del data_career2019['salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2019年4月</th>\n",
       "      <th>2020年4月</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>career</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ADAS</th>\n",
       "      <td>19763.0</td>\n",
       "      <td>23194.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Android开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13834.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAE</th>\n",
       "      <td>11820.0</td>\n",
       "      <td>15260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CTO</th>\n",
       "      <td>35555.0</td>\n",
       "      <td>37325.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cocos2d-x开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DSP</th>\n",
       "      <td>17655.0</td>\n",
       "      <td>17100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETL</th>\n",
       "      <td>13576.0</td>\n",
       "      <td>13385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FPGA</th>\n",
       "      <td>15536.0</td>\n",
       "      <td>17277.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GIS</th>\n",
       "      <td>11850.0</td>\n",
       "      <td>12635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTML5开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13187.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hadoop工程师</th>\n",
       "      <td>17204.0</td>\n",
       "      <td>17567.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MES</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SLAM</th>\n",
       "      <td>22129.0</td>\n",
       "      <td>18548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unity3D</th>\n",
       "      <td>13671.0</td>\n",
       "      <td>14367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unity3d开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16836.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Web前端开发</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iOS开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15954.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>人工智能</th>\n",
       "      <td>20967.0</td>\n",
       "      <td>22718.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>信号处理</th>\n",
       "      <td>14420.0</td>\n",
       "      <td>15223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>分布式</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20393.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>前端开发</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>区块链开发</th>\n",
       "      <td>21231.0</td>\n",
       "      <td>19650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>反作弊算法工程师</th>\n",
       "      <td>32916.0</td>\n",
       "      <td>38125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图像处理工程师</th>\n",
       "      <td>16715.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>图像算法工程师</th>\n",
       "      <td>18544.0</td>\n",
       "      <td>19506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据</th>\n",
       "      <td>17373.0</td>\n",
       "      <td>16305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大数据开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12868.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小程序开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>技术主管</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16655.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>推荐算法工程师</th>\n",
       "      <td>33898.0</td>\n",
       "      <td>30856.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>搜索算法工程师</th>\n",
       "      <td>34562.0</td>\n",
       "      <td>27740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>敏捷教练</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28833.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>数据科学家</th>\n",
       "      <td>31403.0</td>\n",
       "      <td>27149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器人</th>\n",
       "      <td>14423.0</td>\n",
       "      <td>14980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器学习</th>\n",
       "      <td>25165.0</td>\n",
       "      <td>21181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>机器视觉工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12914.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>架构师</th>\n",
       "      <td>25254.0</td>\n",
       "      <td>25314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深度学习工程师</th>\n",
       "      <td>24402.0</td>\n",
       "      <td>21939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏客户端开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14910.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16754.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>游戏服务端开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>爬虫开发工程师</th>\n",
       "      <td>14136.0</td>\n",
       "      <td>13550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>生物信息</th>\n",
       "      <td>10766.0</td>\n",
       "      <td>12275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>移动开发工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>15725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>算法工程师</th>\n",
       "      <td>19432.0</td>\n",
       "      <td>19352.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>系统分析员</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12909.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>系统工程师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9781.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>系统架构师</th>\n",
       "      <td>14794.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>系统架构设计师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>编译器开发工程师</th>\n",
       "      <td>25814.0</td>\n",
       "      <td>23685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>网站架构设计师</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12016.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>自然语言处理（NLP）</th>\n",
       "      <td>24551.0</td>\n",
       "      <td>23640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>芯片</th>\n",
       "      <td>18894.0</td>\n",
       "      <td>25416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>视觉软件工程师</th>\n",
       "      <td>13881.0</td>\n",
       "      <td>14607.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>软件工程师</th>\n",
       "      <td>12847.0</td>\n",
       "      <td>13341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>遥感</th>\n",
       "      <td>12712.0</td>\n",
       "      <td>13410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>驱动工程师</th>\n",
       "      <td>16129.0</td>\n",
       "      <td>17630.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                2019年4月  2020年4月\n",
       "career                          \n",
       "ADAS            19763.0  23194.0\n",
       "Android开发工程师        NaN  13834.0\n",
       "CAE             11820.0  15260.0\n",
       "CTO             35555.0  37325.0\n",
       "Cocos2d-x开发工程师      NaN  16209.0\n",
       "DSP             17655.0  17100.0\n",
       "ETL             13576.0  13385.0\n",
       "FPGA            15536.0  17277.0\n",
       "GIS             11850.0  12635.0\n",
       "HTML5开发工程师          NaN  13187.0\n",
       "Hadoop工程师       17204.0  17567.0\n",
       "MES                 NaN  12466.0\n",
       "SLAM            22129.0  18548.0\n",
       "Unity3D         13671.0  14367.0\n",
       "Unity3d开发工程师        NaN  16836.0\n",
       "Web前端开发             NaN  12488.0\n",
       "iOS开发工程师            NaN  15954.0\n",
       "人工智能            20967.0  22718.0\n",
       "信号处理            14420.0  15223.0\n",
       "分布式                 NaN  20393.0\n",
       "前端开发                NaN  11469.0\n",
       "区块链开发           21231.0  19650.0\n",
       "反作弊算法工程师        32916.0  38125.0\n",
       "图像处理工程师         16715.0      NaN\n",
       "图像算法工程师         18544.0  19506.0\n",
       "大数据             17373.0  16305.0\n",
       "大数据开发工程师            NaN  12868.0\n",
       "小程序开发工程师            NaN   9823.0\n",
       "技术主管                NaN  16655.0\n",
       "推荐算法工程师         33898.0  30856.0\n",
       "搜索算法工程师         34562.0  27740.0\n",
       "敏捷教练                NaN  28833.0\n",
       "数据科学家           31403.0  27149.0\n",
       "机器人             14423.0  14980.0\n",
       "机器学习            25165.0  21181.0\n",
       "机器视觉工程师             NaN  12914.0\n",
       "架构师             25254.0  25314.0\n",
       "深度学习工程师         24402.0  21939.0\n",
       "游戏客户端开发工程师          NaN  14910.0\n",
       "游戏开发工程师             NaN  16754.0\n",
       "游戏服务端开发工程师          NaN  14209.0\n",
       "爬虫开发工程师         14136.0  13550.0\n",
       "生物信息            10766.0  12275.0\n",
       "移动开发工程师             NaN  15725.0\n",
       "算法工程师           19432.0  19352.0\n",
       "系统分析员               NaN  12909.0\n",
       "系统工程师               NaN   9781.0\n",
       "系统架构师           14794.0      NaN\n",
       "系统架构设计师             NaN  20303.0\n",
       "编译器开发工程师        25814.0  23685.0\n",
       "网站架构设计师             NaN  12016.0\n",
       "自然语言处理（NLP）     24551.0  23640.0\n",
       "芯片              18894.0  25416.0\n",
       "视觉软件工程师         13881.0  14607.0\n",
       "软件工程师           12847.0  13341.0\n",
       "遥感              12712.0  13410.0\n",
       "驱动工程师           16129.0  17630.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_career2019.join(data_career, how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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